A modified multi-class U-Net CNN is used to segment the key anatomical structures.
This U-Net is a variant of that introduced in [1]}, modified to use \(128{\times }128\)  input and output, `SAME' padding, a single convolution per encoder and decoder layer (rather than two in the original design), down sampling to a resolution of only \(4{\times }4\)  pixels, and a batch normalisation layer before each ReLu function. These modifications reduce the model's tendency to overfit, a key consideration given the restrictive size of our training set. A batch size of 4 and an ADAM optimizer (learning rate: 0.001) are used with the cross entropy loss function.
Data augmentation reduces further the risk of over-fitting, and is conducted in a random order, with the degree of each modification determined randomly within the following ranges. Overall image brightness: \(0.5\) –\(1.5\) , Gaussian blur: sigma \(0.0\) –\(2.0\) , \(x\) -axis or \(y\) -axis scaling: \(0.9\) –\(1.1\) , \(x\) -axis or \(y\) -axis translation: 0–10%, rotation: \({-15}^{\circ }\)  to \(15^{\circ }\) .
